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We perform linear, logistic, and cox regression using the base functions lm(), glm(), and coxph() in the R software and the 'survival' package. Likewise, we can use ols(), lrm() and cph() from the 'rms' package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. 'base.rms' package can help you to switch between the two sets of commands easily.
| Version: | 1.0 |
| Imports: | rms, survival, do, splines, stats |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-08-01 |
| DOI: | 10.32614/CRAN.package.base.rms |
| Author: | Jing Zhang [aut, cre], Zhi Jin [aut] |
| Maintainer: | Jing Zhang <zj391120 at 163.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | base.rms results |
| Reference manual: | base.rms.pdf |
| Vignettes: |
tutorial version |
| Package source: | base.rms_1.0.tar.gz |
| Windows binaries: | r-devel: base.rms_1.0.zip, r-release: base.rms_1.0.zip, r-oldrel: base.rms_1.0.zip |
| macOS binaries: | r-release (arm64): base.rms_1.0.tgz, r-oldrel (arm64): base.rms_1.0.tgz, r-release (x86_64): base.rms_1.0.tgz, r-oldrel (x86_64): base.rms_1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.